Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Antihormonal-Treatment Status Affects 68Ga-PSMA-HBED-CC PET Biodistribution in Patients with Prostate Cancer.

Androgen deprivation therapy (ADT) is known to influence the prostate-specific membrane antigen (PSMA) expression of prostate cancer, potentially complicating the interpretation of PSMA ligand PET findings and affecting PSMA radioligand therapy. However, the impact of ADT on PSMA ligand biodistribution in nontumorous organs is not well understood. Methods: Men (n = 112) with histologically proven prostate cancer who underwent 68Ga-PSMA-HBED-CC (68Ga-PSMA-11) PET/CT between November 2015 and July 2021 at the Medical University Vienna with known ADT status were retrospectively recruited. Fifty-six patients were on gonadotropin-releasing hormone-interfering ADT at the time of imaging (ADT group), whereas 56 patients with no history of ADT served as a control group. Physiologically PSMA-expressing organs (salivary glands, kidneys, liver, and spleen) were delineated, and their uptake was compared according to their data distributions. Multivariate regression analysis assessed the relationship between renal, hepatic, splenic, and salivary gland uptake and the explanatory variables metabolic tumor volume, glomerular filtration rate, and ADT status. Results: ADT was associated with lower levels of PSMA uptake in the kidneys (SUVmean: Δ[ADT - control] = -7.89; 95% CI, -10.73 to -5.04; P < 0.001), liver (SUVpeak: Δ[ADT - control] = -2.3; 95% CI, -5.72 to -0.93; P = 0.003), spleen (SUVpeak: Δ[ADT - control] = -1.27; 95% CI, -3.61 to -0.16; P = 0.033), and salivary glands (SUVmean: Δ[ADT - control] = -1.04; 95% CI, -2.48 to -0.13; P = 0.027). In a multivariate analysis, ADT was found to be associated with lower renal (SUVmean: ß = -7.95; 95% CI, -11.06 to -4.84; P < 0.0001), hepatic (SUVpeak: ß = -7.85; 95% CI, -11.78 to -3.91; P < 0.0001), splenic (SUVpeak: ß = -5.83; 95% CI, -9.95 to -1.7; P = 0.006), and salivary gland (SUVmean: ß = -1.47; 95% CI, -2.76 to -0.17; P = 0.027) uptake. A higher glomerular filtration rate was associated with a higher renal SUVmean (ß = 0.16; 95% CI, 0.05 to 0.26; P = 0.0034). Conclusion: These findings suggest that ADT systemically modulates PSMA expression, which may have implications for treatment-optimizing and side-effect-minimizing strategies for PSMA radioligand therapies, particularly those using more potent 225Ac-labeled PSMA conjugates.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.